Overview

Dataset statistics

Number of variables14
Number of observations1390
Missing cells4429
Missing cells (%)22.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory882.4 B

Variable types

Numeric2
Text12

Alerts

department has 151 (10.9%) missing valuesMissing
city has 36 (2.6%) missing valuesMissing
state has 972 (69.9%) missing valuesMissing
country has 48 (3.5%) missing valuesMissing
zip code has 918 (66.0%) missing valuesMissing
email has 1221 (87.8%) missing valuesMissing
others has 1072 (77.1%) missing valuesMissing
author_rank has 190 (13.7%) zerosZeros

Reproduction

Analysis started2024-06-01 19:39:02.519388
Analysis finished2024-06-01 19:39:07.164801
Duration4.65 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

pubmed_id
Real number (ℝ)

Distinct148
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37710110
Minimum37596718
Maximum37742171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2024-06-01T19:39:07.344707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum37596718
5-th percentile37596786
Q137741761
median37741914
Q337742025
95-th percentile37742153
Maximum37742171
Range145453
Interquartile range (IQR)264

Descriptive statistics

Standard deviation60090.846
Coefficient of variation (CV)0.0015934943
Kurtosis-0.15778245
Mean37710110
Median Absolute Deviation (MAD)119
Skewness-1.3573605
Sum5.2417053 × 1010
Variance3.6109097 × 109
MonotonicityNot monotonic
2024-06-01T19:39:07.640239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37596837 43
 
3.1%
37742025 33
 
2.4%
37741970 28
 
2.0%
37596786 27
 
1.9%
37742137 27
 
1.9%
37742093 26
 
1.9%
37596839 26
 
1.9%
37741822 24
 
1.7%
37741978 23
 
1.7%
37596793 22
 
1.6%
Other values (138) 1111
79.9%
ValueCountFrequency (%)
37596718 6
 
0.4%
37596774 16
1.2%
37596779 11
0.8%
37596780 4
 
0.3%
37596783 8
 
0.6%
37596784 2
 
0.1%
37596785 22
1.6%
37596786 27
1.9%
37596787 17
1.2%
37596790 6
 
0.4%
ValueCountFrequency (%)
37742171 8
0.6%
37742170 3
 
0.2%
37742169 2
 
0.1%
37742168 7
0.5%
37742167 4
 
0.3%
37742166 7
0.5%
37742165 7
0.5%
37742163 13
0.9%
37742161 5
 
0.4%
37742160 4
 
0.3%

author_rank
Real number (ℝ)

ZEROS 

Distinct33
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5928058
Minimum0
Maximum35
Zeros190
Zeros (%)13.7%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2024-06-01T19:39:07.934401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3.5
Q37
95-th percentile12
Maximum35
Range35
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.4902525
Coefficient of variation (CV)0.97767088
Kurtosis7.931301
Mean4.5928058
Median Absolute Deviation (MAD)2.5
Skewness2.1217082
Sum6384
Variance20.162367
MonotonicityNot monotonic
2024-06-01T19:39:08.210238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 190
13.7%
1 181
13.0%
2 168
12.1%
3 156
11.2%
4 131
9.4%
5 115
8.3%
6 99
7.1%
7 71
 
5.1%
8 68
 
4.9%
9 51
 
3.7%
Other values (23) 160
11.5%
ValueCountFrequency (%)
0 190
13.7%
1 181
13.0%
2 168
12.1%
3 156
11.2%
4 131
9.4%
5 115
8.3%
6 99
7.1%
7 71
 
5.1%
8 68
 
4.9%
9 51
 
3.7%
ValueCountFrequency (%)
35 1
0.1%
34 1
0.1%
33 1
0.1%
32 1
0.1%
31 1
0.1%
30 1
0.1%
29 1
0.1%
28 1
0.1%
24 2
0.1%
23 2
0.1%
Distinct776
Distinct (%)55.8%
Missing0
Missing (%)0.0%
Memory size87.2 KiB
2024-06-01T19:39:08.801346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length25
Median length18
Mean length5.9820144
Min length2

Characters and Unicode

Total characters8315
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique503 ?
Unique (%)36.2%

Sample

1st rowTalbot
2nd rowTalbot
3rd rowPerkins
4th rowTallon
5th rowDawkins
ValueCountFrequency (%)
wang 31
 
2.2%
chen 25
 
1.7%
zhang 23
 
1.6%
liu 20
 
1.4%
li 18
 
1.3%
xu 15
 
1.0%
huang 12
 
0.8%
de 12
 
0.8%
yang 11
 
0.8%
tang 11
 
0.8%
Other values (786) 1257
87.6%
2024-06-01T19:39:09.676654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1078
 
13.0%
n 636
 
7.6%
e 630
 
7.6%
i 609
 
7.3%
r 460
 
5.5%
o 446
 
5.4%
u 384
 
4.6%
h 343
 
4.1%
l 287
 
3.5%
s 260
 
3.1%
Other values (60) 3182
38.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8315
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1078
 
13.0%
n 636
 
7.6%
e 630
 
7.6%
i 609
 
7.3%
r 460
 
5.5%
o 446
 
5.4%
u 384
 
4.6%
h 343
 
4.1%
l 287
 
3.5%
s 260
 
3.1%
Other values (60) 3182
38.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8315
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1078
 
13.0%
n 636
 
7.6%
e 630
 
7.6%
i 609
 
7.3%
r 460
 
5.5%
o 446
 
5.4%
u 384
 
4.6%
h 343
 
4.1%
l 287
 
3.5%
s 260
 
3.1%
Other values (60) 3182
38.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8315
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1078
 
13.0%
n 636
 
7.6%
e 630
 
7.6%
i 609
 
7.3%
r 460
 
5.5%
o 446
 
5.4%
u 384
 
4.6%
h 343
 
4.1%
l 287
 
3.5%
s 260
 
3.1%
Other values (60) 3182
38.3%
Distinct913
Distinct (%)65.7%
Missing1
Missing (%)0.1%
Memory size87.1 KiB
2024-06-01T19:39:10.201196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length20
Median length16
Mean length6.5205184
Min length1

Characters and Unicode

Total characters9057
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique609 ?
Unique (%)43.8%

Sample

1st rowJack S
2nd rowJack S
3rd rowDean R
4th rowChristine M
5th rowTony G
ValueCountFrequency (%)
m 56
 
3.3%
a 26
 
1.5%
j 18
 
1.1%
s 16
 
1.0%
c 15
 
0.9%
r 12
 
0.7%
d 12
 
0.7%
maría 11
 
0.7%
yu 10
 
0.6%
n 9
 
0.5%
Other values (919) 1493
89.0%
2024-06-01T19:39:11.019191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1105
 
12.2%
i 874
 
9.6%
n 820
 
9.1%
e 609
 
6.7%
o 468
 
5.2%
h 383
 
4.2%
r 367
 
4.1%
u 339
 
3.7%
289
 
3.2%
g 281
 
3.1%
Other values (53) 3522
38.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9057
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1105
 
12.2%
i 874
 
9.6%
n 820
 
9.1%
e 609
 
6.7%
o 468
 
5.2%
h 383
 
4.2%
r 367
 
4.1%
u 339
 
3.7%
289
 
3.2%
g 281
 
3.1%
Other values (53) 3522
38.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9057
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1105
 
12.2%
i 874
 
9.6%
n 820
 
9.1%
e 609
 
6.7%
o 468
 
5.2%
h 383
 
4.2%
r 367
 
4.1%
u 339
 
3.7%
289
 
3.2%
g 281
 
3.1%
Other values (53) 3522
38.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9057
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1105
 
12.2%
i 874
 
9.6%
n 820
 
9.1%
e 609
 
6.7%
o 468
 
5.2%
h 383
 
4.2%
r 367
 
4.1%
u 339
 
3.7%
289
 
3.2%
g 281
 
3.1%
Other values (53) 3522
38.9%
Distinct197
Distinct (%)14.2%
Missing1
Missing (%)0.1%
Memory size79.3 KiB
2024-06-01T19:39:11.623440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length3
Median length1
Mean length1.2455004
Min length1

Characters and Unicode

Total characters1730
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)6.8%

Sample

1st rowJS
2nd rowJS
3rd rowDR
4th rowCM
5th rowTG
ValueCountFrequency (%)
s 111
 
8.0%
a 86
 
6.2%
y 86
 
6.2%
j 84
 
6.0%
m 83
 
6.0%
l 57
 
4.1%
h 54
 
3.9%
d 48
 
3.5%
k 47
 
3.4%
r 43
 
3.1%
Other values (187) 690
49.7%
2024-06-01T19:39:12.484292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 188
 
10.9%
J 155
 
9.0%
S 149
 
8.6%
A 144
 
8.3%
Y 110
 
6.4%
R 81
 
4.7%
L 80
 
4.6%
H 77
 
4.5%
K 77
 
4.5%
D 76
 
4.4%
Other values (19) 593
34.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 188
 
10.9%
J 155
 
9.0%
S 149
 
8.6%
A 144
 
8.3%
Y 110
 
6.4%
R 81
 
4.7%
L 80
 
4.6%
H 77
 
4.5%
K 77
 
4.5%
D 76
 
4.4%
Other values (19) 593
34.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 188
 
10.9%
J 155
 
9.0%
S 149
 
8.6%
A 144
 
8.3%
Y 110
 
6.4%
R 81
 
4.7%
L 80
 
4.6%
H 77
 
4.5%
K 77
 
4.5%
D 76
 
4.4%
Other values (19) 593
34.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 188
 
10.9%
J 155
 
9.0%
S 149
 
8.6%
A 144
 
8.3%
Y 110
 
6.4%
R 81
 
4.7%
L 80
 
4.6%
H 77
 
4.5%
K 77
 
4.5%
D 76
 
4.4%
Other values (19) 593
34.3%
Distinct746
Distinct (%)53.7%
Missing1
Missing (%)0.1%
Memory size259.4 KiB
2024-06-01T19:39:12.980963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length465
Median length195
Mean length120.14399
Min length21

Characters and Unicode

Total characters166880
Distinct characters100
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique479 ?
Unique (%)34.5%

Sample

1st rowCardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, UK.
2nd rowCentre for Health, Activity and Wellbeing Research, Cardiff Metropolitan University, Cardiff, UK.
3rd rowDepartment of Sport Science, University of Innsbruck, Innsbruck, Austria.
4th rowCentre for Heart, Lung and Vascular Health, School of Health and Exercise Sciences, University of British Columbia Okanagan, Kelowna, Canada.
5th rowCentre for Heart, Lung and Vascular Health, School of Health and Exercise Sciences, University of British Columbia Okanagan, Kelowna, Canada.
ValueCountFrequency (%)
of 1983
 
9.5%
university 898
 
4.3%
and 738
 
3.5%
department 509
 
2.4%
china 415
 
2.0%
medicine 332
 
1.6%
medical 313
 
1.5%
hospital 292
 
1.4%
laboratory 271
 
1.3%
research 269
 
1.3%
Other values (1852) 14905
71.2%
2024-06-01T19:39:13.786091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19525
 
11.7%
e 12940
 
7.8%
a 12185
 
7.3%
i 11431
 
6.8%
n 10945
 
6.6%
o 9367
 
5.6%
t 8453
 
5.1%
r 7918
 
4.7%
, 5877
 
3.5%
l 5649
 
3.4%
Other values (90) 62590
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 166880
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
19525
 
11.7%
e 12940
 
7.8%
a 12185
 
7.3%
i 11431
 
6.8%
n 10945
 
6.6%
o 9367
 
5.6%
t 8453
 
5.1%
r 7918
 
4.7%
, 5877
 
3.5%
l 5649
 
3.4%
Other values (90) 62590
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 166880
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
19525
 
11.7%
e 12940
 
7.8%
a 12185
 
7.3%
i 11431
 
6.8%
n 10945
 
6.6%
o 9367
 
5.6%
t 8453
 
5.1%
r 7918
 
4.7%
, 5877
 
3.5%
l 5649
 
3.4%
Other values (90) 62590
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 166880
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
19525
 
11.7%
e 12940
 
7.8%
a 12185
 
7.3%
i 11431
 
6.8%
n 10945
 
6.6%
o 9367
 
5.6%
t 8453
 
5.1%
r 7918
 
4.7%
, 5877
 
3.5%
l 5649
 
3.4%
Other values (90) 62590
37.5%

department
Text

MISSING 

Distinct426
Distinct (%)34.4%
Missing151
Missing (%)10.9%
Memory size131.4 KiB
2024-06-01T19:39:14.240952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length416
Median length83
Mean length45.110573
Min length4

Characters and Unicode

Total characters55892
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)13.8%

Sample

1st rowSchool of Sport and Health Sciences
2nd rowCentre for Health, Activity and Wellbeing Research
3rd rowDepartment of Sport Science
4th rowCentre for Heart, Lung and Vascular Health
5th rowCentre for Heart, Lung and Vascular Health
ValueCountFrequency (%)
of 986
 
13.7%
department 477
 
6.6%
and 458
 
6.4%
laboratory 223
 
3.1%
key 178
 
2.5%
medicine 174
 
2.4%
for 138
 
1.9%
center 134
 
1.9%
research 113
 
1.6%
engineering 100
 
1.4%
Other values (591) 4212
58.6%
2024-06-01T19:39:15.006633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5954
 
10.7%
e 5499
 
9.8%
o 4211
 
7.5%
a 4202
 
7.5%
n 3789
 
6.8%
t 3738
 
6.7%
i 3423
 
6.1%
r 3394
 
6.1%
l 2093
 
3.7%
c 1917
 
3.4%
Other values (65) 17672
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5954
 
10.7%
e 5499
 
9.8%
o 4211
 
7.5%
a 4202
 
7.5%
n 3789
 
6.8%
t 3738
 
6.7%
i 3423
 
6.1%
r 3394
 
6.1%
l 2093
 
3.7%
c 1917
 
3.4%
Other values (65) 17672
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5954
 
10.7%
e 5499
 
9.8%
o 4211
 
7.5%
a 4202
 
7.5%
n 3789
 
6.8%
t 3738
 
6.7%
i 3423
 
6.1%
r 3394
 
6.1%
l 2093
 
3.7%
c 1917
 
3.4%
Other values (65) 17672
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5954
 
10.7%
e 5499
 
9.8%
o 4211
 
7.5%
a 4202
 
7.5%
n 3789
 
6.8%
t 3738
 
6.7%
i 3423
 
6.1%
r 3394
 
6.1%
l 2093
 
3.7%
c 1917
 
3.4%
Other values (65) 17672
31.6%
Distinct418
Distinct (%)30.2%
Missing8
Missing (%)0.6%
Memory size124.6 KiB
2024-06-01T19:39:15.566642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length99
Median length64
Mean length31.719247
Min length3

Characters and Unicode

Total characters43836
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique177 ?
Unique (%)12.8%

Sample

1st rowCardiff Metropolitan University
2nd rowCardiff Metropolitan University
3rd rowUniversity of Innsbruck
4th rowUniversity of British Columbia Okanagan
5th rowUniversity of British Columbia Okanagan
ValueCountFrequency (%)
university 792
 
14.0%
of 484
 
8.5%
medical 215
 
3.8%
hospital 184
 
3.2%
and 163
 
2.9%
institute 120
 
2.1%
national 111
 
2.0%
research 99
 
1.7%
medicine 84
 
1.5%
center 81
 
1.4%
Other values (623) 3342
58.9%
2024-06-01T19:39:16.720504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4293
 
9.8%
i 4193
 
9.6%
e 3652
 
8.3%
n 3185
 
7.3%
a 2976
 
6.8%
t 2669
 
6.1%
o 2392
 
5.5%
r 2321
 
5.3%
s 2006
 
4.6%
l 1635
 
3.7%
Other values (70) 14514
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43836
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4293
 
9.8%
i 4193
 
9.6%
e 3652
 
8.3%
n 3185
 
7.3%
a 2976
 
6.8%
t 2669
 
6.1%
o 2392
 
5.5%
r 2321
 
5.3%
s 2006
 
4.6%
l 1635
 
3.7%
Other values (70) 14514
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43836
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4293
 
9.8%
i 4193
 
9.6%
e 3652
 
8.3%
n 3185
 
7.3%
a 2976
 
6.8%
t 2669
 
6.1%
o 2392
 
5.5%
r 2321
 
5.3%
s 2006
 
4.6%
l 1635
 
3.7%
Other values (70) 14514
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43836
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4293
 
9.8%
i 4193
 
9.6%
e 3652
 
8.3%
n 3185
 
7.3%
a 2976
 
6.8%
t 2669
 
6.1%
o 2392
 
5.5%
r 2321
 
5.3%
s 2006
 
4.6%
l 1635
 
3.7%
Other values (70) 14514
33.1%

city
Text

MISSING 

Distinct266
Distinct (%)19.6%
Missing36
Missing (%)2.6%
Memory size86.9 KiB
2024-06-01T19:39:17.341388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length21
Median length19
Mean length7.2776957
Min length4

Characters and Unicode

Total characters9854
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)6.4%

Sample

1st rowCardiff
2nd rowCardiff
3rd rowInnsbruck
4th rowKelowna
5th rowKelowna
ValueCountFrequency (%)
wuhan 42
 
2.9%
hangzhou 39
 
2.7%
madrid 34
 
2.4%
nanning 32
 
2.2%
shanghai 27
 
1.9%
oxford 26
 
1.8%
kathmandu 24
 
1.7%
kunming 22
 
1.5%
rotterdam 22
 
1.5%
hohhot 21
 
1.5%
Other values (282) 1157
80.0%
2024-06-01T19:39:18.555296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1295
 
13.1%
n 1023
 
10.4%
i 665
 
6.7%
e 623
 
6.3%
o 593
 
6.0%
h 510
 
5.2%
r 490
 
5.0%
u 373
 
3.8%
g 368
 
3.7%
d 350
 
3.6%
Other values (53) 3564
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9854
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1295
 
13.1%
n 1023
 
10.4%
i 665
 
6.7%
e 623
 
6.3%
o 593
 
6.0%
h 510
 
5.2%
r 490
 
5.0%
u 373
 
3.8%
g 368
 
3.7%
d 350
 
3.6%
Other values (53) 3564
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9854
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1295
 
13.1%
n 1023
 
10.4%
i 665
 
6.7%
e 623
 
6.3%
o 593
 
6.0%
h 510
 
5.2%
r 490
 
5.0%
u 373
 
3.8%
g 368
 
3.7%
d 350
 
3.6%
Other values (53) 3564
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9854
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1295
 
13.1%
n 1023
 
10.4%
i 665
 
6.7%
e 623
 
6.3%
o 593
 
6.0%
h 510
 
5.2%
r 490
 
5.0%
u 373
 
3.8%
g 368
 
3.7%
d 350
 
3.6%
Other values (53) 3564
36.2%

state
Text

MISSING 

Distinct75
Distinct (%)17.9%
Missing972
Missing (%)69.9%
Memory size56.2 KiB
2024-06-01T19:39:19.139588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length20
Median length17
Mean length5.8971292
Min length1

Characters and Unicode

Total characters2465
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)3.6%

Sample

1st rowOhio
2nd rowDolnoslaskie
3rd rowWielkopolskie
4th rowLubuskie
5th rowWielkopolskie
ValueCountFrequency (%)
ca 45
 
9.0%
province 26
 
5.2%
jiangsu 23
 
4.6%
new 19
 
3.8%
western 17
 
3.4%
md 16
 
3.2%
hubei 16
 
3.2%
mongolia 15
 
3.0%
ny 15
 
3.0%
inner 15
 
3.0%
Other values (70) 292
58.5%
2024-06-01T19:39:20.231846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 225
 
9.1%
i 209
 
8.5%
a 204
 
8.3%
e 178
 
7.2%
o 129
 
5.2%
r 119
 
4.8%
A 100
 
4.1%
g 92
 
3.7%
s 83
 
3.4%
81
 
3.3%
Other values (41) 1045
42.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2465
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 225
 
9.1%
i 209
 
8.5%
a 204
 
8.3%
e 178
 
7.2%
o 129
 
5.2%
r 119
 
4.8%
A 100
 
4.1%
g 92
 
3.7%
s 83
 
3.4%
81
 
3.3%
Other values (41) 1045
42.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2465
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 225
 
9.1%
i 209
 
8.5%
a 204
 
8.3%
e 178
 
7.2%
o 129
 
5.2%
r 119
 
4.8%
A 100
 
4.1%
g 92
 
3.7%
s 83
 
3.4%
81
 
3.3%
Other values (41) 1045
42.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2465
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 225
 
9.1%
i 209
 
8.5%
a 204
 
8.3%
e 178
 
7.2%
o 129
 
5.2%
r 119
 
4.8%
A 100
 
4.1%
g 92
 
3.7%
s 83
 
3.4%
81
 
3.3%
Other values (41) 1045
42.4%

country
Text

MISSING 

Distinct60
Distinct (%)4.5%
Missing48
Missing (%)3.5%
Memory size84.7 KiB
2024-06-01T19:39:20.803430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length26
Mean length6.3703428
Min length2

Characters and Unicode

Total characters8549
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.7%

Sample

1st rowUK
2nd rowUK
3rd rowAustria
4th rowCanada
5th rowCanada
ValueCountFrequency (%)
china 365
22.3%
usa 190
 
11.6%
japan 89
 
5.4%
spain 57
 
3.5%
nepal 57
 
3.5%
germany 53
 
3.2%
of 51
 
3.1%
republic 50
 
3.1%
uk 48
 
2.9%
canada 47
 
2.9%
Other values (54) 629
38.4%
2024-06-01T19:39:21.938022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1305
15.3%
n 879
 
10.3%
i 680
 
8.0%
e 503
 
5.9%
h 436
 
5.1%
C 425
 
5.0%
l 323
 
3.8%
p 307
 
3.6%
294
 
3.4%
S 291
 
3.4%
Other values (38) 3106
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8549
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1305
15.3%
n 879
 
10.3%
i 680
 
8.0%
e 503
 
5.9%
h 436
 
5.1%
C 425
 
5.0%
l 323
 
3.8%
p 307
 
3.6%
294
 
3.4%
S 291
 
3.4%
Other values (38) 3106
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8549
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1305
15.3%
n 879
 
10.3%
i 680
 
8.0%
e 503
 
5.9%
h 436
 
5.1%
C 425
 
5.0%
l 323
 
3.8%
p 307
 
3.6%
294
 
3.4%
S 291
 
3.4%
Other values (38) 3106
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8549
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1305
15.3%
n 879
 
10.3%
i 680
 
8.0%
e 503
 
5.9%
h 436
 
5.1%
C 425
 
5.0%
l 323
 
3.8%
p 307
 
3.6%
294
 
3.4%
S 291
 
3.4%
Other values (38) 3106
36.3%

zip code
Text

MISSING 

Distinct105
Distinct (%)22.2%
Missing918
Missing (%)66.0%
Memory size57.7 KiB
2024-06-01T19:39:22.633566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length12
Median length6
Mean length5.7478814
Min length2

Characters and Unicode

Total characters2713
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)7.6%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10
ValueCountFrequency (%)
430070 42
 
8.5%
310006 25
 
5.0%
200011 23
 
4.6%
650093 22
 
4.4%
ox3 18
 
3.6%
90033 18
 
3.6%
550001 17
 
3.4%
310016 13
 
2.6%
28034 13
 
2.6%
28029 12
 
2.4%
Other values (99) 293
59.1%
2024-06-01T19:39:23.407001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 861
31.7%
1 375
13.8%
3 323
 
11.9%
2 185
 
6.8%
5 165
 
6.1%
4 138
 
5.1%
6 130
 
4.8%
7 125
 
4.6%
8 119
 
4.4%
9 110
 
4.1%
Other values (25) 182
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2713
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 861
31.7%
1 375
13.8%
3 323
 
11.9%
2 185
 
6.8%
5 165
 
6.1%
4 138
 
5.1%
6 130
 
4.8%
7 125
 
4.6%
8 119
 
4.4%
9 110
 
4.1%
Other values (25) 182
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2713
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 861
31.7%
1 375
13.8%
3 323
 
11.9%
2 185
 
6.8%
5 165
 
6.1%
4 138
 
5.1%
6 130
 
4.8%
7 125
 
4.6%
8 119
 
4.4%
9 110
 
4.1%
Other values (25) 182
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2713
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 861
31.7%
1 375
13.8%
3 323
 
11.9%
2 185
 
6.8%
5 165
 
6.1%
4 138
 
5.1%
6 130
 
4.8%
7 125
 
4.6%
8 119
 
4.4%
9 110
 
4.1%
Other values (25) 182
 
6.7%

email
Text

MISSING 

Distinct117
Distinct (%)69.2%
Missing1221
Missing (%)87.8%
Memory size51.1 KiB
2024-06-01T19:39:23.826397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length36
Median length30
Mean length20.852071
Min length12

Characters and Unicode

Total characters3524
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)49.7%

Sample

1st rowmarcin.zawadzki@umed.wroc.pl
2nd rowskropins@ump.edu.pl
3rd rowwojciechleppert@wp.pl
4th rowkwt@tobis.pl
5th rownjmulss@njmu.edu.cn
ValueCountFrequency (%)
xinquanjiang2011@126.com 4
 
2.4%
natalie.claunch@usda.gov 4
 
2.4%
xu_lei@kust.edu.cn 4
 
2.4%
haoew@gxtcmu.edu.cn 3
 
1.8%
gsxiong@njau.edu.cn 3
 
1.8%
afelipe@ub.edu 3
 
1.8%
bryansolitude@gmail.com 3
 
1.8%
huangxiaolong@gznu.edu.cn 3
 
1.8%
hychen_1201@163.com 3
 
1.8%
paul.habert@ap-hm.fr 3
 
1.8%
Other values (107) 136
80.5%
2024-06-01T19:39:24.528375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 285
 
8.1%
a 269
 
7.6%
e 218
 
6.2%
n 214
 
6.1%
i 212
 
6.0%
o 200
 
5.7%
u 199
 
5.6%
m 183
 
5.2%
c 180
 
5.1%
@ 169
 
4.8%
Other values (38) 1395
39.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 285
 
8.1%
a 269
 
7.6%
e 218
 
6.2%
n 214
 
6.1%
i 212
 
6.0%
o 200
 
5.7%
u 199
 
5.6%
m 183
 
5.2%
c 180
 
5.1%
@ 169
 
4.8%
Other values (38) 1395
39.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 285
 
8.1%
a 269
 
7.6%
e 218
 
6.2%
n 214
 
6.1%
i 212
 
6.0%
o 200
 
5.7%
u 199
 
5.6%
m 183
 
5.2%
c 180
 
5.1%
@ 169
 
4.8%
Other values (38) 1395
39.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 285
 
8.1%
a 269
 
7.6%
e 218
 
6.2%
n 214
 
6.1%
i 212
 
6.0%
o 200
 
5.7%
u 199
 
5.6%
m 183
 
5.2%
c 180
 
5.1%
@ 169
 
4.8%
Other values (38) 1395
39.6%

others
Text

MISSING 

Distinct143
Distinct (%)45.0%
Missing1072
Missing (%)77.1%
Memory size71.7 KiB
2024-06-01T19:39:24.986650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length271
Median length86
Mean length56.5
Min length4

Characters and Unicode

Total characters17967
Distinct characters87
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)25.2%

Sample

1st rowSchool of Physics and Astronomy
2nd rowSchool of Physics and Astronomy
3rd rowSchool of Physics and Astronomy
4th rowSchool of Physics and Astronomy
5th rowSchool of Physics and Astronomy
ValueCountFrequency (%)
of 247
 
10.3%
and 90
 
3.7%
research 75
 
3.1%
center 57
 
2.4%
university 54
 
2.2%
zhejiang 52
 
2.2%
hospital 51
 
2.1%
school 42
 
1.7%
oral 39
 
1.6%
for 37
 
1.5%
Other values (409) 1660
69.1%
2024-06-01T19:39:25.740819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2110
 
11.7%
e 1468
 
8.2%
a 1307
 
7.3%
i 1300
 
7.2%
n 1159
 
6.5%
o 1094
 
6.1%
t 945
 
5.3%
r 890
 
5.0%
s 681
 
3.8%
l 659
 
3.7%
Other values (77) 6354
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17967
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2110
 
11.7%
e 1468
 
8.2%
a 1307
 
7.3%
i 1300
 
7.2%
n 1159
 
6.5%
o 1094
 
6.1%
t 945
 
5.3%
r 890
 
5.0%
s 681
 
3.8%
l 659
 
3.7%
Other values (77) 6354
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17967
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2110
 
11.7%
e 1468
 
8.2%
a 1307
 
7.3%
i 1300
 
7.2%
n 1159
 
6.5%
o 1094
 
6.1%
t 945
 
5.3%
r 890
 
5.0%
s 681
 
3.8%
l 659
 
3.7%
Other values (77) 6354
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17967
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2110
 
11.7%
e 1468
 
8.2%
a 1307
 
7.3%
i 1300
 
7.2%
n 1159
 
6.5%
o 1094
 
6.1%
t 945
 
5.3%
r 890
 
5.0%
s 681
 
3.8%
l 659
 
3.7%
Other values (77) 6354
35.4%

Interactions

2024-06-01T19:39:03.967670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-01T19:39:03.209337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-01T19:39:04.372368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-01T19:39:03.595291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-06-01T19:39:26.007693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
author_rankpubmed_id
author_rank1.000-0.104
pubmed_id-0.1041.000

Missing values

2024-06-01T19:39:04.963422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-01T19:39:05.770884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-01T19:39:06.863336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

pubmed_idauthor_ranklast_namefore_nameinitialsaffiliationdepartmentname_of_universitycitystatecountryzip codeemailothers
0377421370TalbotJack SJSCardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, UK.School of Sport and Health SciencesCardiff Metropolitan UniversityCardiffNaNUKNaNNaNNaN
1377421370TalbotJack SJSCentre for Health, Activity and Wellbeing Research, Cardiff Metropolitan University, Cardiff, UK.Centre for Health, Activity and Wellbeing ResearchCardiff Metropolitan UniversityCardiffNaNUKNaNNaNNaN
2377421371PerkinsDean RDRDepartment of Sport Science, University of Innsbruck, Innsbruck, Austria.Department of Sport ScienceUniversity of InnsbruckInnsbruckNaNAustriaNaNNaNNaN
3377421372TallonChristine MCMCentre for Heart, Lung and Vascular Health, School of Health and Exercise Sciences, University of British Columbia Okanagan, Kelowna, Canada.Centre for Heart, Lung and Vascular HealthUniversity of British Columbia OkanaganKelownaNaNCanadaNaNNaNNaN
4377421373DawkinsTony GTGCentre for Heart, Lung and Vascular Health, School of Health and Exercise Sciences, University of British Columbia Okanagan, Kelowna, Canada.Centre for Heart, Lung and Vascular HealthUniversity of British Columbia OkanaganKelownaNaNCanadaNaNNaNNaN
5377421374DouglasAndrew J MAJMCardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, UK.School of Sport and Health SciencesCardiff Metropolitan UniversityCardiffNaNUKNaNNaNNaN
6377421374DouglasAndrew J MAJMCentre for Health, Activity and Wellbeing Research, Cardiff Metropolitan University, Cardiff, UK.Centre for Health, Activity and Wellbeing ResearchCardiff Metropolitan UniversityCardiffNaNUKNaNNaNNaN
7377421375BeckerlegRyanRCardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, UK.Brain Research Imaging Centre (CUBRIC)Cardiff UniversityCardiffNaNUKNaNNaNSchool of Physics and Astronomy
8377421376CroftsAndrewACardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, UK.Brain Research Imaging Centre (CUBRIC)Cardiff UniversityCardiffNaNUKNaNNaNSchool of Physics and Astronomy
9377421377WrightMelissa EMECardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, UK.Brain Research Imaging Centre (CUBRIC)Cardiff UniversityCardiffNaNUKNaNNaNSchool of Physics and Astronomy
pubmed_idauthor_ranklast_namefore_nameinitialsaffiliationdepartmentname_of_universitycitystatecountryzip codeemailothers
1380375967746SinclairRodney DRDSinclair Dermatology, Melbourne, Australia.DermatologyDermatologyMelbourneNaNAustraliaNaNNaNNaN
1381375967746SinclairRodney DRDUniversity of Melbourne, Melbourne, Australia.NaNUniversity of MelbourneMelbourneNaNAustraliaNaNNaNNaN
1382375967746SinclairRodney DRDSinclair Dermatology.NaNSinclair DermatologyNaNNaNNaNNaNNaNNaN
1383375967746SinclairRodney DRDUniversity of Melbourne, Melbourne,Australia.NaNUniversity of MelbourneMelbourneNaNAustraliaNaNNaNNaN
1384375967186XiShuiqingSDepartment of Cardiology, The Fourth Hospital of Harbin Medical University, 150000, Harbin, P. R. China.Department of CardiologyHarbin Medical UniversityHarbinNaNP. R. China150000NaNThe Fourth Hospital
1385375967187HongXiaojianXDepartment of Cardiology, The Fourth Hospital of Harbin Medical University, 150000, Harbin, P. R. China.Department of CardiologyHarbin Medical UniversityHarbinNaNP. R. China150000NaNThe Fourth Hospital
1386375967188ZhouMeifangMDepartment of Nuclear Medicine, The Fourth Hospital of Harbin Medical University, 150000, Harbin, P. R. China.Department of Nuclear MedicineHarbin Medical UniversityHarbinNaNP. R. China150000NaNNaN
1387375967188ZhouMeifangMNHC Key Laboratory of Molecular Probe and Targeted Theranostics, Molecular Imaging Research Center (MIRC) of Harbin Medical University, 150000, Harbin, P. R. China.NHC Key Laboratory of Molecular Probe and Targeted Theranostics, Molecular Imaging Research Center (MIRC)Harbin Medical UniversityHarbinNaNP. R. China150000NaNNaN
1388375967189WangHaoyuHDepartment of Nuclear Medicine, The Fourth Hospital of Harbin Medical University, 150000, Harbin, P. R. China.Department of Nuclear MedicineHarbin Medical UniversityHarbinNaNP. R. China150000NaNNaN
1389375967189WangHaoyuHNHC Key Laboratory of Molecular Probe and Targeted Theranostics, Molecular Imaging Research Center (MIRC) of Harbin Medical University, 150000, Harbin, P. R. China.NHC Key Laboratory of Molecular Probe and Targeted Theranostics, Molecular Imaging Research Center (MIRC)Harbin Medical UniversityHarbinNaNP. R. China150000NaNNaN